Acceptance Of Robots Research Articles

Machine learning of robots in tourism and hospitality: interactive technology acceptance model (iTAM) – cutting edge

El Machine Learning (Aprendizaje Automático) de robots en turismo y hotelería: Modelo de Aceptación de Tecnología Interactiva (iTAM): tecnología de puntaObjetivoEl objetivo de este estudio, es analizar la aceptación que tienen los robots avanzados de Inteligencia Artificial (IA) por parte de los consumidores de hoteles y turismo y proporcionar una tipología y un marco conceptual para apoyar la investigación futura sobre la aplicabilidad avanzada de estos robots.Diseño/metodología/enfoqueEsta investigación, revisa los casos actuales de uso de IA y estudios del Modelo de Aceptación de Tecnología (TAM) y propone: el Modelo de Aceptación de Tecnología Interactiva (iTAM) para identificar los determinantes clave que estimulan las percepciones del consumidor sobre la aceptación avanzada de la tecnología de robots.ResultadosLos principales prototipos de robots avanzados se identificaron mediante la revisión de los estudios TAM y de IA sobre robots que se utilizan actualmente en la industria del turismo y la hostelería. Esta investigación, encontró que a medida que las tecnologías testadas en los estudios TAM, se han mejorado mediante la incorporación de sistemas altamente interactivos, aumentando la capacidad y mejorando la usabilidad de la interfaz; se ha vuelto más importante examinar la interactividad percibida de la tecnología para los modelos avanzados de aceptación de robots. Los ejemplos de usos avanzados de robots, indican que cada aplicación de aprendizaje automático varía el rendimiento de la tarea de los robots y la interacción con los consumidores. La realización de estudios experimentales y la medición de la interactividad de los robots avanzados son vitales para futuras investigaciones.Originalidad/valorEste es el primer estudio sobre como los consumidores del sector turístico y hotelero aceptan los robots de IA basado en aplicaciones de machine learning (aprendizaje automático). El marco iTAM proporciona constructos fundamentales para futuros estudios sobre los factores que influyen en el consumidor a la hora de aceptar los robots de IA como tecnología innovadora. iTAM se podría aplicar a experimentos empíricos e investigaciones con el objetivo de generar estrategias a largo plazo y consejos específicos para implementar y administrar varios robots avanzados.Palabras claveiTAM, Aprendizaje automático, Robot de inteligencia artificial, Aceptación de tecnología, Interactividad percibida, Tipo de robot IATipo de papel Papel conceptual

A Framework of Hybrid Force/Motion Skills Learning for Robots

Human factors and human-centered design philosophy are highly desired in today's robotics applications such as human-robot interaction (HRI). Several studies showed that endowing robots of human-like interaction skills can not only make them more likeable but also improve their performance. In particular, skill transfer by imitation learning can increase the usability and acceptability of robots by users without computer programming skills. In fact, besides positional information, muscle stiffness of the human arm and contact force with the environment also play important roles in understanding and generating human-like manipulation behaviors for robots, e.g., in physical HRI and teleoperation. To this end, we present a novel robot learning framework based on dynamic movement primitives (DMPs), taking into consideration both the positional and contact force profiles for human-robot skills transferring. Distinguished from the conventional method involving only the motion information, the proposed framework combines two sets of DMPs, which are built to model the motion trajectory and the force variation of the robot manipulator, respectively. Thus, a hybrid force/motion control approach is taken to ensure the accurate tracking and reproduction of the desired positional and force motor skills. Meanwhile, in order to simplify the control system, a momentum-based force observer is applied to estimate the contact force instead of employing force sensors. To deploy the learned motion-force robot manipulation skills to a broader variety of tasks, the generalization of these DMP models in actual situations is also considered. Comparative experiments have been conducted using a Baxter robot to verify the effectiveness of the proposed learning framework on real-world scenarios like cleaning a table.

Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots

In the last years, there have been rapid developments in social robotics, which bring about the prospect of their application as persuasive robots to support behavior change. In order to guide related developments and pave the way for their adoption, it is important to understand the factors that influence the acceptance of social robots as persuasive agents. This study extends the technology acceptance model by including measures of social responses. The social responses include trusting belief, compliance, liking, and psychological reactance. Using the Wizard of Oz method, a laboratory experiment was conducted to evaluate user acceptance and social responses towards a social robot called SociBot. This robot was used as a persuasive agent in making decisions in donating to charities. Using partial least squares method, results showed that trusting beliefs and liking towards the robot significantly add the predictive power of the acceptance model of persuasive robots. However, due to the limitations of the study design, psychological reactance and compliance were not found to contribute to the prediction of persuasive robots’ acceptance. Implications for the development of persuasive robots are discussed.

What Makes a Social Robot Good at Interacting with Humans?

This paper discusses the nuances of a social robot, how and why social robots are becoming increasingly significant, and what they are currently being used for. This paper also reflects on the current design of social robots as a means of interaction with humans and also reports potential solutions about several important questions around the futuristic design of these robots. The specific questions explored in this paper are: “Do social robots need to look like living creatures that already exist in the world for humans to interact well with them?”; “Do social robots need to have animated faces for humans to interact well with them?”; “Do social robots need to have the ability to speak a coherent human language for humans to interact well with them?” and “Do social robots need to have the capability to make physical gestures for humans to interact well with them?”. This paper reviews both verbal as well as nonverbal social and conversational cues that could be incorporated into the design of social robots, and also briefly discusses the emotional bonds that may be built between humans and robots. Facets surrounding acceptance of social robots by humans and also ethical/moral concerns have also been discussed.

Frontline Employees' Acceptance of and Resistance to Service Robots in Stationary Retail - An Exploratory Interview Study

Due to rising online competition, increasing cost pressure and cross-channel customer journeys, stationary retail has tried to develop innovative value propositions and co-create value with customers through new technologies, which are expected to profoundly change the stationary retail’s service systems. Among other technologies, service robots are said to have the potential to revitalise interactive value creation in stationary retail. However, the integration of such technologies poses new challenges. Prior research has looked at customers’ acceptance of service robots in stationary retail settings, but few studies have explored their counterparts – the frontline employees’ (FLEs) perspective. Yet, FLEs’ acceptance of service robots is crucial to implement service robots for retail innovation. To explore FLEs’ acceptance of and resistance to service robots, a qualitative exploratory interview study is conducted. It identifies decisive aspects, amongst others loss of status or role incongruency. The findings extend prior studies on technology acceptance and resistance and reveal i.a. that FLEs perceive service robots as both a threat and potential support. Moreover, they feel hardly involved in the co-creation of use cases for a service robot, although they are willing to contribute.

Service Robots, Customers, and Service Employees: What Can We Learn from the Academic Literature and Where are the Gaps?

Purpose – Robots are predicted to have a profound impact on the service sector. The emergence of robots has attracted increasing interest from business scholars and practitioners alike. In this article, we undertake a systematic review of the business literature about the impact of service robots on customers and employees with the objective of guiding future research. Design/methodology/approach – We analyzed the literature on service robots as they relate to customers and employees in business journals listed in the Financial Times top 50 journals plus all journals covered in the cross-disciplinary SERVSIG literature alerts. Findings – The analysis of the identified studies yielded multiple observations about the impact of service robots on customers (e.g., overarching frameworks on acceptance and usage of service robots; characteristics of service robots and anthropomorphism; and potential for enhanced and deteriorated service experiences) and service employees (e.g., employee benefits such as reduced routine work, enhanced productivity and job satisfaction; potential negative consequences such as loss of autonomy and a range of negative psychological outcomes; opportunities for human-robot collaboration; job insecurity; and robot-related upskilling and development requirements). We also conclude that current research on service robots is fragmented, is largely conceptual in nature, and focused on the initial adoption stage. We feel that more research is needed to build an overarching theory. In addition, more empirical research is needed, especially on the long(er)- term usage service robots on actual behaviors, the well-being, and potential downsides and (ethical) risks for customers and service employees. Research limitations – Our review focused on the business and service literature. Future work may want to include additional literature streams, including those in computer science, engineering, and information systems. Originality/value – This article is the first to synthesize the business and service literature on the impact of service robots on customers and employees.

Factors that influence robot acceptance

With the development of technology, robots as the main carrier of artificial intelligence will appear more and more in human life and work. However, for ordinary people, robots are still relatively new things. Under such a background, whether people accept robots, under what conditions robots are more acceptable to people, and what kind of robots are more acceptable to people are all important issues that need to be explored. Therefore, this paper focuses on the factors that affect people’s acceptance of robots. Specifically, we systematically review the influencing factors of robot acceptance from three aspects: Robot factors, human factors and human-robot interaction factors. Firstly, there is no doubt that people’s acceptance of robots is affected by the factors pertaining to robot itself. Compared with human factors, robot factors are more controllable and practical for robot designers, producers and sellers, and thus are currently the most concerned issue for the public and researchers. Secondly, characteristics of robots such as their visual features, tactile features, auditory features and behavioral features can significantly affect people’s acceptance of robots. Among these characteristics of robots, the anthropomorphism of robot appearance is a hot research issue. A series of studies indicates that the appropriate degree of anthropomorphism can increase people’s acceptance of robot, but inappropriate degree of anthropomorphism can trigger uncanny valley effect, which in turn significantly decrease robot acceptance. Capabilities of robots, such as their instrumental and social abilities have also been proved to play an important in affecting people’s acceptance of robots. The instrumental capabilities of robots are undoubtedly the basic abilities, which include not only utilitarian factors (e.g., perceived usefulness and perceived ease of use), but also hedonic factors (e.g., perceived enjoyment). The social capabilities of robots are mainly reflected on their abilities to socialize with people, which include communicating with human and expressing their social cues such as emotions and vulnerability. Secondly, human factors (e.g., demographic variables and psychological variables) influencing people’s acceptance of robots should not be neglected. Many studies demonstrate that an individual’s demographic characteristics such as age, gender, education and cultural background play an important role in robot acceptance. In general, young people, males, and those with higher education are more likely to accept robots. Individual’s psychological characteristics such as personality, personal innovativeness and self-efficacy have also been shown to have an impact on people’s acceptance of robots. Thirdly, in addition to robot factors and human factors, human-robot interaction factors affecting robot acceptance have also been paid more and more attention by researchers. Lots of studies indicate that robot-related experience can significantly influence people’s acceptance of robots. Previous experience with robots has a positive effect on users’ attitudes towards robots, trust in robots and their own self-esteem. Meanwhile, people’s perception of robots (e.g., perception of robots’ warmth, competence and social presence) is also important. Researchers have suggested that people’s perception of robots’ warmth and competence is not only relevant to their attitudes towards robots, but also can predict their specific emotional response and behavioral tendency. In sum, we systematically review possible influencing factors of robot acceptance, which include robot factors, human factors and human-robot interaction factors. Based on the theory of mind perception, we suggest that people’s acceptance of robots are both positive and negative. On the one hand, people over-trust robots because of the strong capabilities of robots. On the other hand, people also attack robots because of ignoring robots’ experience. Having considered all possible factors, we believe that anthropomorphism may be the core variable that affects people’s acceptance of robots, and the ultimate problem of robot acceptance is necessarily moral issues concerning robots.

Health Professional and Workers Attitudes Towards the Use of Social Robots for Older Adults in Long-Term Care

The aim of this study was (a) to modify, translate and validate the Chinese version of attitudes towards the use of social robot (ATTUSR-C) questionnaire for use with Taiwanese health personnel; and (b) investigate the attitudes of Taiwanese health personnel in long-term care towards the use of social robots for older adults. The attitudes of health personnel towards social robots can affect the acceptability of social robots for older adults. An investigation of health personnel’s ATTUSRs and the development of a validated Chinese questionnaire is needed. A cross-sectional design was used to conduct this multi-phase study. Data collection was from November 2017 to May 2018. Content validity, internal consistency reliability, and factor analysis of the ATTUSR-C questionnaire were evaluated. Purposive sampling was used. All recruited participants received an email containing study information and a URL link to the survey. The ATTUSR-C questionnaire had good validity and reliability. A total of 416 health professionals responded to the online survey. Most health personnel had positive ATTUSRs in long-term care facilities as they viewed social robots as beneficial and practical in psychosocial care for older adults. Positive ATTUSRs can increase acceptance and utilisation of social robots. This study strives to support nursing work by providing insights into health personnel’s perceptions of social robots, in order to integrate social robots into the care and lives of older adults.

Learning and Comfort in Human–Robot Interaction: A Review

Collaborative robots provide prospective and great solutions to human–robot cooperative tasks. In this paper, we present a comprehensive review for two significant topics in human–robot interaction: robots learning from demonstrations and human comfort. The collaboration quality between the human and the robot has been improved largely by taking advantage of robots learning from demonstrations. Human teaching and robot learning approaches with their corresponding applications are investigated in this review. We also discuss several important issues that need to be paid attention to and addressed in the human–robot teaching–learning process. After that, the factors that may affect human comfort in human–robot interaction are described and discussed. Moreover, the measures utilized to improve human acceptance of robots and human comfort in human–robot interaction are also presented and discussed.

Children’s acceptance of social robots

Abstract Social robots progressively enter children’s lives, but little is known about children’s acceptance of social robots and its antecedents. To fill this research gap, this narrative review surveyed 34 articles on child-robot interaction published between 2000 and 2017. We focused on robot, user, and interaction characteristics as potential antecedents of children’s intentional and behavioral social robot acceptance. In general, children readily accept robots. However, we found that social, adaptive robot behavior, children’s sex and age, as well as frequency of the interaction seem to affect acceptance. Additionally, we found various theoretical and methodological shortcomings in the field. The review concludes with recommendations and directions for future research on children’s acceptance of social robots.

Social cues in robot interaction, trust and acceptance

Social cues in robot interaction, trust and acceptance

THE UNCANNY VALLEY REVISITED: AGE-RELATED DIFFERENCE AND THE EFFECT OF FUNCTION TYPE

Due to declined birthrate and the increased aging population, solving the problem of labor shortage has become important. Introducing robotic labors could effectively help older adults’ daily lives. However, older adults’ acceptance of robots was lower than younger adults. Robot’s appearance might be one of the reasons. The Uncanny Valley (UV) refers to the phenomenon that people rate more positively as robots become more humanlike, but only up to a certain point; as it approaches near-perfect similarity of human appearance, likeability drops and forms an uncanny valley. Nonetheless, previous results supporting the UV were mainly from younger adults. We examined whether the UV is also applicable for older and middle-aged adults. We also examined whether the acceptance of function (companion vs. service) would change based on robot appearance, and whether robot-induced traits have any relation with the acceptance of robot function. We asked younger (N= 80, age 18-39), middle-aged (N= 87, age 40-59), and older (N= 88, age 60-87) adults to view each picture of 84 robots and evaluate their impression of each robot and intention of use regarding robot function. Contrary to the UV found for younger and middle-aged adults, older adults did not show UV–they preferred humanlike over non-humanlike robots, regardless of the robot function. Scores on each trait–except for authoritativeness–showed positive correlations with the acceptance of functions. These findings imply that the design of assistive robots should take UV into consideration by customizing robots’ appearances and functions to different age groups.

Robot acceptance model for care (RAM-care): A principled approach to the intention to use care robots

Robots are emerging in welfare services, and organizations require information on whether novel technologies are approved among staff. On the basis of technology acceptance theories, this study proposes a model that adds a principled approach to the intention to use care robots. Data of 544 professionals with care robot experience were collected. The use intention was predicted by usefulness, enjoyment, social influence, and attitude. Respondents who found robots useful and accepted by their colleagues were more likely to view robot use as consistent with their personal values. The care robot acceptance model supports consideration of the profession-specific context in robotization.

Human\u2013Robot Collaboration Acceptance Model: Development and Comparison for Germany, Japan, China and the USA

The use of robots in the national economy—especially in industrialized countries—is growing. At the same time, the interdependency between humans and robots is getting increasingly closer: they are engaging in direct contact with each other as more and more organizations let robots and humans work hand-in-hand. One factor that predicts successful human–robot interdependency is the acceptance of the robot by the human. Generally, only when an innovative assistive working system covers human needs and expectations, it is perceived to be useful and hence accepted. Furthermore, it has been found that cultural context has an impact on human–robot interaction, as people feel more comfortable interacting with a robot in a culturally normative way. Therefore this paper aims at presenting a human–robot collaboration acceptance model (HRCAM) with regard to the collaboration between humans and robots that is based on prior acceptance models, while also considering technology affinity and ethical, legal and social implications. Additionally, similarities and differences in robot acceptance are shown for four selected countries—both in comparison to the overall human–robot collaboration acceptance model and between the countries. The HRCAM additionally shows which variables influence perceived usefulness and perceived ease of use, and thus behavioral intention to use and use behavior. A further distinction is made between anchor variables, which can be influenced in the long term, and adjustment variables, which can be influenced in the short to medium term. The model therefore offers practitioners in the field of human–robot collaboration recommendations to increase the acceptance of robots.

Questionnaires to Measure Acceptability of Social Robots: A Critical Review

Understanding user perceptions is particularly important in developing social robots, which tend to have a high degree of interaction with humans. However, psychometric measures of robot acceptability have only recently started to become available. The present critical review outlines the psychometrically validated questionnaires to measure social acceptability factors related to social robots. Using an iterative search strategy, articles were identified that reported on the development of such questionnaires as well as information about their psychometric properties. Six questionnaires were identified that provide researchers with options varying in length, content, and factor structure. Two of these questionnaires inquire about attitudes and anxieties related to robots, while two others capture a larger range of attitudes that extends to positive and neutral aspects as well. One of the questionnaires reviewed here was specific to inquiring about ethical issues related to the use of social robots for therapy with children with autism, and the last one was designed to provide an assessment of expectations of participants prior to interacting with a robot. Overall, the use of robot acceptability measures is still relatively new, and further psychometric work is necessary to provide confidence in the validity and reliability of these scales.

Elderly’s acceptance of companion robots from the perspective of user factors

Taiwan has entered the aged society in March 2018, meaning that more social and technological resources are needed to solve the problems related to the elderly’s companion service. Companion robots are considered a solution to effectively meet the elderly’s service needs for family escort. However, little is known about the elderly’s acceptance of companion robots. The purpose of this study is to explore the elderly’s acceptance of companion robots from the perspective of user factors. The research was carried out by a mixed method of interviews and questionnaires. Independent sample t test and one-way analysis of variance were used for analysis. The results showed that there were significant differences in the attitude and perceived usefulness of companion robots in terms of education level, living conditions, professional background and technical experience. The research found that the elderly living with parents, with master’s (or doctor’s) education, medical professional background and experience in the use of scientific and technological products expressed more positive attitudes in the responses to the items on the constructs of attitude and perceived usefulness, while the attitude of those with primary school education and humanities professional background, with no experience in scientific and technological products, was relatively negative. Research shows that the acceptance of companion robots by the elderly was affected to some extent by user factors. These findings can provide reference for robot designers, industrial designers and other researchers.

Attributing Blame to Robots: I. The Influence of Robot Autonomy.

The research examined how humans attribute blame to humans, nonautonomous robots, autonomous robots, or environmental factors for scenarios in which errors occur. When robots and humans serve on teams, human perception of their technological team members can be a critical component of successful cooperation, especially when task completion fails. Participants read a set of scenarios that described human-robot team task failures. Separate scenarios were written to emphasize the role of the human, the robot, or environmental factors in producing the task failure. After reading each scenario, the participants allocated blame for the failure among the human, robot, and environmental factors. In general, the order of amount of blame was humans, robots, and environmental factors. If the scenario described the robot as nonautonomous, the participants attributed almost as little blame to them as to the environmental factors; in contrast, if the scenario described the robot as autonomous, the participants attributed almost as much blame to them as to the human. We suggest that humans use a hierarchy of blame in which robots are seen as partial social actors, with the degree to which people view them as social actors depending on the degree of autonomy. The acceptance of robots by human co-workers will be a function of the attribution of blame when errors occur in the workplace. The present research suggests that greater autonomy for the robot will result in greater attribution of blame in work tasks.

The Influence of Feedback Type in Robot-Assisted Training

Robot-assisted training, where social robots can be used as motivational coaches, provides an interesting application area. This paper examines how feedback given by a robot agent influences the various facets of participant experience in robot-assisted training. Specifically, we investigated the effects of feedback type on robot acceptance, sense of safety and security, attitude towards robots and task performance. In the experiment, 23 older participants performed basic arm exercises with a social robot as a guide and received feedback. Different feedback conditions were administered, such as flattering, positive and negative feedback. Our results suggest that the robot with flattering and positive feedback was appreciated by older people in general, even if the feedback did not necessarily correspond to objective measures such as performance. Participants in these groups felt better about the interaction and the robot.

Robotics for Customer Service: A Useful Complement or an Ultimate Substitute?

We propose a conceptual framework that includes the antecedents and consequences of firms’ adopting and integrating robotics into their customer service operations. Drawing insights from literature on customer service, technology marketing, and computer science, our proposed framework elaborates on the concept of the degree of robotics adoption (DRA) as well as the antecedents (employee acceptance of robots and customer acceptance of robots) and multiple sequential consequences (service quality, customer long-term performance, and customer engagement) of DRA. We also discuss how the nature of the firm (Business to Consumer versus Business-to-Business, i.e., B2C vs. B2B), service characteristics (utilitarian vs. hedonic), and brand positioning (low equity vs. high equity) might moderate the relationship between DRA and service quality. Further, we provide actionable guidance for managers to adopt and integrate robotics into their customer service operations.

A Hybrid Joint/Cartesian DMP-Based Approach for Obstacle Avoidance of Anthropomorphic Assistive Robots

Anthropomorphic criteria are widely adopted for developing socially interactive robots since they can improve human capability to interpret and predict robot motion, with an impact on robot acceptability and human–robot interaction safety. Learning by demonstration approaches based on dynamic movement primitives are a suitable solution for planning the robot motion in human-like fashion and endow robots with generalization capabilities and robustness against perturbation. Objective of this work is to propose a new formulation of the learning by demonstration approach based on dynamic movement primitives (DMPs), called hybrid joint/Cartesian DMPs, for redundant robots with the twofold purpose of avoiding obstacles on the path and obtaining anthropomorphic motion in the joint as well as the task space. The proposed approach, suitable for assistive purposes, was experimentally validated on the anthropomorphic robot arm Kuka Light Weight Robot 4+. Trajectories recorded by an optoelectronic system (i.e. the BTS Smart D) from human demonstrators performing point-to-point reaching and pouring tasks were adopted to teach the robot how to move. A comparative analysis with current methods of learning by demonstration in the literature, i.e. Cartesian DMP and inverse kinematics, was performed. Performance indicators were introduced to (1) assess the robot accuracy of the motion performing, (2) evaluate the level of anthropomorphism of the computed motion, (3) measure the success rate in obstacle avoidance. Moreover, questionnaires were administered to ten human subjects who observed robot motion. The obtained results demonstrated that the proposed approach guarantees anthropomorphic motion of the robot both in the joint and in the task spaces ensuring obstacle avoidance along the robot kinematic chain. In particular, the anthropomorphic robot, while operating with the proposed method, exhibits behaviours in the joint space that are similar to the one recorded during the demonstration and has a level of anthropomorphism rather higher than the one obtained with Cartesian DMP and inverse kinematics (a configuration of the robot joints within the physiological limits is always ensured and the maximum convex hull created between the human and the robot arm is 0.0085 m\(^3\)). In presence of obstacles, an acceptable level of the Cartesian accuracy (\(Position~err<0.005\) m and \(Orientation~err<0.02\) rad) is achieved. The obstacles are avoided with a 100% of success. The questionnaire results showed that the users feel more comfortable and less nervous to interact with a robot that moves in human-like manner.